Recognition of human abnormal behavior based on semi-supervised learning

The video that needed to carry out the recognition of the abnormal human behaviors were mostly the unlabelled image sequences,the traditional supervised recognition methods usually could not well retrieve the behavior features and thus did not have a high recognition rates.It was presented a human abnormal behavior recognition method based on semi-supervised learning.It first extended the set of the labeling data based on the DTW distance by using self-training,then,the extended set of image samples was used to train the corresponding HMM,finally,the recognition of the abnormal behavior was carried out.The experiment results demonstrated that this method was effective with a high recognition rate.